, has developed and validated two software environments for the analysis, simulation and design of tensegrity robots. These tools, along with new control methodologies and the modular hardware components developed to validate them, are presented as a system for the design of actuated tensegrity structures. As evidenced from their appearance in many biological systems, tensegrity ('tensile-integrity') structures have unique physical properties that make them ideal for interaction with uncertain environments. Yet, these characteristics make design and control of bioinspired tensegrity robots extremely challenging. This work presents the progress our tools have made in tackling the design and control challenges of spherical tensegrity structures. We focus on this shape since it lends itself to rolling locomotion. The results of our analyses include multiple novel control approaches for mobility and terrain interaction of spherical tensegrity structures that have been tested in simulation. A hardware prototype of a spherical six-bar tensegrity, the Reservoir Compliant Tensegrity Robot, is used to empirically validate the accuracy of simulation.

Embodiment has led to a revolution in robotics by not thinking of the robot body and its controller as two separate units, but taking into account the interaction of the body with its environment. By investigating the effect of the body on the overall control computation, it has been suggested that the body is effectively performing computations, leading to the term morphological computation. Recent work has linked this to the field of reservoir computing, allowing one to endow morphologies with a theory of universal computation. In this work, we study a family of highly dynamic body structures, called tensegrity structures, controlled by one of the simplest kinds of "brains." These structures can be used to model biomechanical systems at different scales. By analyzing this extreme instantiation of compliant structures, we demonstrate the existence of a spectrum of choices of how to implement control in the body-brain composite. We show that tensegrity structures can maintain complex gaits with linear feedback control and that external feedback can intrinsically be integrated in the control loop. The various linear learning rules we consider differ in biological plausibility, and no specific assumptions are made on how to implement the feedback in a physical system.

We present a tool that aids in the modeling of optical circuits, both in the frequency and in the time domain. The tool is based on the definition of a node, which can have both an instantaneous input-output relation, as well as different state variables (e.g. temperature and carrier density) and differential equations for these states. Furthermore, each node has access to part of its input history, allowing the creation of delay lines or digital filters. Additionally, a node can contain sub-nodes, allowing the creation of hierarchical networks. This tool can be used in numerous applications such as frequency-domain analysis of optical ring filters, time-domain analysis of optical amplifiers, microdisks and microcavities. Although we mainly use this tool to model optical circuits, it can also be used to model other classes of dynamical systems, such as electrical circuits and neural networks.

Abstract. 200 words A biologically-inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e.g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulation. But the capacity of the model to work on a real robot platform had not been tested.This article presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cueguided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment -recognized as new contexts -, and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performances and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such brain-inspired meta-controller may provide an advancement for learning architectures in robotics.A biologically inspired meta-control navigation system for the Psikharpax rat robot 2